okapies · June 12, 2019 03:21 · Jun 12, 2019
diff --git a/train_mnist_logreport.py b/train_mnist_logreport.py
@@ -0,0 +1,86 @@
+#!/usr/bin/env python
+import argparse
+
+import chainer
+import chainer.functions as F
+import chainer.links as L
+from chainer import training
+from chainer.training import extensions
+
+import numpy as np
+
+
+# Network definition
+class MLP(chainer.Chain):
+
+    def __init__(self, n_units, n_out):
+        super(MLP, self).__init__()
+        with self.init_scope():
+            # the size of the inputs to each layer will be inferred
+            self.l1 = L.Linear(None, n_units)  # n_in -> n_units
+            self.l2 = L.Linear(None, n_units)  # n_units -> n_units
+            self.l3 = L.Linear(None, n_out)  # n_units -> n_out
+
+    def forward(self, x):
+        h1 = F.relu(self.l1(x))
+        h2 = F.relu(self.l2(h1))
+        return self.l3(h2)
+
+
+def main():
+    parser = argparse.ArgumentParser(description='Chainer example: MNIST')
+    parser.add_argument('--batchsize', '-b', type=int, default=100,
+                        help='Number of images in each mini-batch')
+    parser.add_argument('--epoch', '-e', type=int, default=20,
+                        help='Number of sweeps over the dataset to train')
+    parser.add_argument('--device', '-d', type=str, default='-1',
+                        help='Device specifier. Either ChainerX device '
+                        'specifier or an integer. If non-negative integer, '
+                        'CuPy arrays with specified device id are used. If '
+                        'negative integer, NumPy arrays are used')
+    parser.add_argument('--out', '-o', default='result',
+                        help='Directory to output the result')
+    parser.add_argument('--unit', '-u', type=int, default=1000,
+                        help='Number of units')
+    args = parser.parse_args()
+
+    device = chainer.get_device(args.device)
+
+    print('Device: {}'.format(device))
+    print('# unit: {}'.format(args.unit))
+    print('# Minibatch-size: {}'.format(args.batchsize))
+    print('# epoch: {}'.format(args.epoch))
+    print('')
+
+    # Set up a neural network to train
+    # Classifier reports softmax cross entropy loss and accuracy at every
+    # iteration, which will be used by the PrintReport extension below.
+    model = L.Classifier(MLP(args.unit, 10))
+    model.to_device(device)
+    device.use()
+
+    # Setup an optimizer
+    optimizer = chainer.optimizers.Adam()
+    optimizer.setup(model)
+
+    # Load the MNIST dataset
+    train, _ = chainer.datasets.get_mnist()
+    train = chainer.datasets.TupleDataset(
+        np.stack([train[0][0]]), np.stack([train[0][1]]))
+
+    train_iter = chainer.iterators.SerialIterator(train, args.batchsize)
+
+    # Set up a trainer
+    updater = training.updaters.StandardUpdater(
+        train_iter, optimizer, device=device)
+    trainer = training.Trainer(updater, (args.epoch, 'epoch'), out=args.out)
+
+    # Write a log of evaluation statistics for each epoch
+    trainer.extend(extensions.LogReport())
+
+    # Run the training
+    trainer.run()
+
+
+if __name__ == '__main__':
+    main()